A Smoke Detection Algorithm with Multi-Texture Feature Exploration Under a Spatio-Temporal Background Model

doi:10.7652/xjtuxb201808011

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A Smoke Detection Algorithm with Multi-Texture Feature Exploration Under a Spatio-Temporal Background Model

更新时间：2025-07-09

- A Smoke Detection Algorithm with Multi-Texture Feature Exploration Under a Spatio-Temporal Background Model
- Vol. 52, Issue 8, Pages: 67-73(2018)
- 作者机构：
  
  天津大学微电子学院,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201808011
  CLC： TP391.41
- Online First：10 August 2018，
  
  Published：2018
- 稿件说明：
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A Smoke Detection Algorithm with Multi-Texture Feature Exploration Under a Spatio-Temporal Background Model[J]. 2018, 52(8): 67-73.
DOI：

A Smoke Detection Algorithm with Multi-Texture Feature Exploration Under a Spatio-Temporal Background Model[J]. 2018, 52(8): 67-73. DOI： 10.7652/xjtuxb201808011.

摘要

针对复杂场景的烟雾检测准确性低等问题

提出了一种基于多种纹理特征的烟雾检测算法。首先

为了提取出完整的烟雾前景区域

在背景建模时融合了视频像素点的时间和空间信息。然后

在研究和改进局部二值图特征的基础上

提出了3种新的具有高辨别力和鲁棒性的纹理特征

分别为梯度局部二值图特征、多量级局部二值图特征以及局部共生二值图特征。通过提取前景区域局部图像块的这3种纹理特征

利用支持向量机分类器进行分类。最终

通过对3种纹理特征的综合决策检测出准确的烟雾区域。在烟雾图像数据库的测试下

该算法的平均检测出率、误报率及错误率分别为0.978、0.014及0.016

与现有最优算法相比

性能分别提高了0.6%、0.97%、0.83%。大量视频实验结果表明

该算法对复杂场景适应性强

检测准确率高

对比现有视频烟雾检测算法检测率提高了2%～4%。

Abstract

A novel smoke detection algorithm based on the multi-texture features is proposed to solve the problem of low detection rate of smoke in complex scenes. In order to extract the complete smoke foreground area

both temporal and spatial information of the pixels are fused in the background modelling process. Three novel discriminative and robust texture features are proposed by carefully studying and improving the local binary pattern feature

and are further utilized for support vector machine training in the foreground patch area. Finally

the accurate smoke area is detected through a comprehensive decision making on these features. Test results on smoke image data sets show that the average detection rate

false alarm rate and error rate of the proposed algorithm are 0.978

0.145 and 0.162

respectively

which gain improvements of 0.6%、0.97% and 0.83%

respectively

compared with the existing optimal algorithm. Extensive experiments on challenging scenes show that the proposed algorithm outperforms other video-based smoke detection methods by 2%- 4% in the detection rate.

关键词

Keywords

references

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